






from __future__ import annotations

__UpperCamelCase   :   Optional[Any]      =    list[list[int]]

# assigning initial values to the grid
__UpperCamelCase   :   Matrix      =    [
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
__UpperCamelCase   :   Matrix      =    [
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	for i in range(9       ):
		if grid[row][i] == n or grid[i][column] == n:
			return False

	for i in range(3       ):
		for j in range(3       ):
			if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
				return False

	return True





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	for i in range(9       ):
		for j in range(9       ):
			if grid[i][j] == 0:
				return i, j
	return None





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	if location := find_empty_location(lowerCamelCase       ):
		__lowercase							,    __lowercase   							=	location
	else:
		# If the location is ``None``, then the grid is solved.
		return grid

	for digit in range(1					,	10       ):
		if is_safe(lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase					,	lowerCamelCase       ):
			__lowercase   							=	digit

			if sudoku(lowerCamelCase       ) is not None:
				return grid

			__lowercase   							=	0

	return None





def 					snake_case	(  lowerCamelCase       ):




	'''simple docstring'''







	for row in grid:
		for cell in row:
			print(lowerCamelCase					,	end=""" """       )
		print()


if __name__ == "__main__":
						# make a copy of grid so that you can compare with the unmodified grid
						for example_grid in (initial_grid, no_solution):
												print("""\nExample grid:\n""" + """=""" * 20)
												print_solution(example_grid)
												print("""\nExample grid solution:""")
												__UpperCamelCase   :   Optional[Any]      =    sudoku(example_grid)
												if solution is not None:
																		print_solution(solution)
												else:
																		print("""Cannot find a solution.""")




import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
		import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin







@require_torch
class 					__lowercase  (						unittest.TestCase		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Optional[Any]      		=					load_tool('''text-to-speech'''						)
														self.tool.setup()




							def       snake_case			(					self						)		->       Union[str, Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					Optional[Any]      		=					self.tool('''hey'''						)
														A      :					List[str]      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






							def       snake_case			(					self						)		->       Optional[Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					int      		=					self.tool('''hey'''						)
														A      :					Any      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






